#!/usr/bin/env python3
# -*- coding: utf-8 -*-

"""
图像处理器 - PyQt6版本
对应原C++项目的多线程图像处理功能
"""

import cv2
import numpy as np
import threading
import time
import queue
from PySide6.QtCore import QThread, Signal, Slot, QMutex, QWaitCondition


class ImageProcessor(QThread):
    """图像处理器类"""

    # 定义信号
    image_ready = Signal(np.ndarray)  # 图像准备就绪信号
    detection_ready = Signal(np.ndarray, list)  # 检测结果准备就绪信号

    def __init__(self, xdma_driver, yolov5_detector=None):
        super().__init__()
        self.xdma_driver = xdma_driver
        self.yolov5_detector = yolov5_detector

        # 线程控制
        self.running = False
        self.paused = False

        # 数据队列
        self.image_queue = queue.Queue(maxsize=10)
        self.result_queue = queue.Queue(maxsize=10)

        # 图像参数
        self.img_height = 720
        self.img_width = 1280
        self.frame_size = self.img_height * self.img_width * 3

        # 互斥锁和条件变量
        self.mutex = QMutex()
        self.condition = QWaitCondition()

        # 图像缓冲区
        self.image_buffer = None

    def run(self):
        """线程主函数"""
        self.running = True
        print("图像处理器线程启动")

        try:
            while self.running:
                # 检查是否暂停
                self.mutex.lock()
                if self.paused:
                    self.condition.wait(self.mutex)
                self.mutex.unlock()

                if not self.running:
                    break

                # 从硬件获取图像
                image = self._capture_image()
                if image is not None:
                    # 发送图像信号
                    self.image_ready.emit(image)

                    # 进行目标检测
                    if self.yolov5_detector:
                        detections = self.yolov5_detector.detect(image)
                        self.detection_ready.emit(image, detections)

                # 控制帧率
                time.sleep(0.033)  # 约30FPS

        except Exception as e:
            print(f"图像处理器错误: {e}")
        finally:
            print("图像处理器线程结束")

    def _capture_image(self):
        """从硬件捕获图像"""
        try:
            if not self.xdma_driver or not self.xdma_driver.devices_opened:
                return None

            # 从DMA读取图像数据
            # 注意：这里需要根据实际的硬件接口实现
            raw_data = self.xdma_driver.read_data(0x30000000, self.frame_size)

            if len(raw_data) == self.frame_size:
                # 转换原始数据为图像
                image = np.frombuffer(raw_data, dtype=np.uint8)
                image = image.reshape((self.img_height, self.img_width, 3))

                # BGR转RGB（如果需要）
                image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

                return image
            else:
                print(f"图像数据大小不匹配: 期望{self.frame_size}, 实际{len(raw_data)}")
                return None

        except Exception as e:
            print(f"图像捕获错误: {e}")
            return None

    def stop(self):
        """停止线程"""
        self.mutex.lock()
        self.running = False
        self.condition.wakeAll()
        self.mutex.unlock()

        # 等待线程结束
        if self.isRunning():
            self.wait()

    def pause(self):
        """暂停线程"""
        self.mutex.lock()
        self.paused = True
        self.mutex.unlock()

    def resume(self):
        """恢复线程"""
        self.mutex.lock()
        self.paused = False
        self.condition.wakeAll()
        self.mutex.unlock()

    def is_paused(self):
        """检查是否暂停"""
        self.mutex.lock()
        paused = self.paused
        self.mutex.unlock()
        return paused


class YOLOv5Detector:
    """YOLOv5检测器类"""

    def __init__(self):
        # YOLOv5模型参数（简化版）
        self.class_names = [
            "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat",
            "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person",
            "pottedplant", "sheep", "sofa", "train", "tvmonitor"
        ]

        # NMS参数
        self.conf_thres = 0.3
        self.iou_thres = 0.5

        # Anchor boxes (简化版)
        self.anchors = [
            [[10, 13], [16, 30], [33, 23]],
            [[30, 61], [62, 45], [59, 119]],
            [[116, 90], [156, 198], [373, 326]]
        ]

        # 特征图尺寸
        self.feature_sizes = [20, 40, 80]

        # 模拟检测结果（实际应该加载训练好的模型）
        self.mock_detections = True

    def detect(self, image):
        """执行目标检测"""
        if self.mock_detections:
            # 返回模拟检测结果
            return self._mock_detection(image)
        else:
            # 这里应该使用实际的YOLOv5模型进行推理
            return []

    def _mock_detection(self, image):
        """模拟检测（用于演示）"""
        detections = []

        # 随机生成一些检测框（仅用于演示）
        height, width = image.shape[:2]

        for i in range(3):  # 生成3个假检测结果
            # 随机位置和大小
            x = np.random.randint(0, width - 100)
            y = np.random.randint(0, height - 100)
            w = np.random.randint(50, 100)
            h = np.random.randint(50, 100)

            # 随机类别和置信度
            class_id = np.random.randint(0, len(self.class_names))
            confidence = np.random.uniform(0.5, 0.9)

            detection = {
                'class_id': class_id,
                'class_name': self.class_names[class_id],
                'confidence': confidence,
                'bbox': [x, y, x + w, y + h]
            }

            detections.append(detection)

        return detections

    def load_model(self, model_path):
        """加载YOLOv5模型"""
        # 这里应该加载实际的模型文件
        # 暂时只打印信息
        print(f"加载模型: {model_path}")
        self.mock_detections = False  # 切换到真实检测模式


def test_image_processor():
    """测试图像处理器"""
    print("测试图像处理器...")

    # 这里需要实际的硬件驱动进行完整测试
    # 暂时只测试YOLOv5检测器
    detector = YOLOv5Detector()

    # 创建假图像进行测试
    test_image = np.random.randint(0, 255, (480, 640, 3), dtype=np.uint8)

    # 执行检测
    detections = detector.detect(test_image)
    print(f"检测到 {len(detections)} 个目标")

    for i, det in enumerate(detections):
        print(f"检测 {i}: {det['class_name']} ({det['confidence']:.2f}) at {det['bbox']}")

    print("图像处理器测试完成")


if __name__ == "__main__":
    test_image_processor()
